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Publication

Credibility-Aware Multimodal Fusion Using Probabilistic Circuits

Sahil Sidheekh; Pranuthi Tenali; Saurabh Mathur; Erik Blasch; Kristian Kersting; Sriraam Natarajan
In: Yingzhen Li; Stephan Mandt; Shipra Agrawal; Mohammad Sadil Khan (Hrsg.). International Conference on Artificial Intelligence and Statistics, AISTATS 2025, Mai Khao, Thailand, 3-5 May 2025. International Conference on Artificial Intelligence and Statistics (AISTATS), Pages 2305-2313, Proceedings of Machine Learning Research, Vol. 258, PMLR, 2025.

Abstract

We consider the problem of late multi-modal fu- sion for discriminative learning. Motivated by noisy, multi-source domains that require under- standing the reliability of each data source, we explore the notion of credibility in the context of multi-modal fusion. We propose a combination function that uses probabilistic circuits (PCs) to combine predictive distributions over individual modalities. We also define a probabilistic measure to evaluate the credibility of each modality via inference queries over the PC. Our experimental evaluation demonstrates that our fusion method can reliably infer credibility while maintaining com- petitive performance with the state-of-the-art.

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